0f571c347b
I'd like to nominate @zzzzwwjj @realliujiaxu @LCAIZJ to join vLLM Ascend
committer team.
@zzzzwwjj
---
- Review Quality:
He has completed 80+reviews since April. 2025, include
https://github.com/vllm-project/vllm-ascend/pull/3232#issuecomment-3506110786,
https://github.com/vllm-project/vllm-ascend/pull/4822#discussion_r2601661204,
https://github.com/vllm-project/vllm-ascend/pull/4768#issuecomment-3644795995
high quality review.
- Sustained Contributions
15+ Valuable bug fix and refactor is very good.
https://github.com/vllm-project/vllm-ascend/pulls?q=is%3Apr+author%3Azzzzwwjj+is%3Aclosed+review%3Aapproved
Continuous optimization of code architecture
https://github.com/vllm-project/vllm-ascend/pulls?q=author%3Azzzzwwjj+is%3Amerged
- Quality Contribution:
https://github.com/vllm-project/vllm-ascend/pull/1229
https://github.com/vllm-project/vllm-ascend/pull/1979
https://github.com/vllm-project/vllm-ascend/pull/4359
https://github.com/vllm-project/vllm-ascend/pull/4878
- Community Involvement:
He lead the https://github.com/vllm-project/vllm-ascend/issues/1147, to
refactor AscendFusedMoE at the first time.
He shared topics about large-scale distributed inference and
reinforcement learning on vLLM-Ascend meetup on August 2nd.
@realliujiaxu
---
- Review Quality:
He has completed about [40+
reviews](https://github.com/vllm-project/vllm-ascend/pulls?q=is%3Apr+commenter%3Arealliujiaxu+-author%3Arealliujiaxu+)
since September, include
https://github.com/vllm-project/vllm-ascend/pull/4868#discussion_r2605549015,
https://github.com/vllm-project/vllm-ascend/pull/2275#discussion_r2268455665.
- Sustained Contributions
He has completed (17
commits)[https://github.com/vllm-project/vllm-ascend/pulls?q=is%3Apr+author%3Arealliujiaxu+is%3Amerged],
continuously optimizing the performance of the MoE model.
- Quality Contribution:
Contributed the Flash Comm1 feature to the community, supporting both
eager and aclgraph execution modes, while compatible with multiple MoE
models including DeepSeek and GLM4.5.
- https://github.com/vllm-project/vllm-ascend/pull/3334
- https://github.com/vllm-project/vllm-ascend/pull/3420
- https://github.com/vllm-project/vllm-ascend/pull/3015
co-author:
- https://github.com/vllm-project/vllm-ascend/pull/3495
- https://github.com/vllm-project/vllm-ascend/pull/4868
- Community Involvement:
1. Completed two major refactors, enabling vllm-ascend to evolve more
rapidly and robustly: [Linear
module](https://github.com/vllm-project/vllm-ascend/pull/2867) and
[rejection
sampler](https://github.com/vllm-project/vllm-ascend/pull/4975)
2. [fixed 8
bugs](https://github.com/vllm-project/vllm-ascend/pulls?q=is%3Apr+author%3Arealliujiaxu+is%3Amerged+bugfix+)
in graph mode, spec decoding and async scheduling.
@LCAIZJ
---
- Review Quality: He's been the go-to reviewer for virtually all PD
disaggregation and KV Pool related PRs, having completed [30+
reviews](https://github.com/vllm-project/vllm-ascend/pulls?q=is%3Apr+commenter%3ALCAIZJ+is%3Aopen+-author%3ALCAIZJ+)
since May 2025. Notable examples include
[discussion_r2553887360](https://github.com/vllm-project/vllm-ascend/pull/4345#discussion_r2553887360),
[issuecomment-3540994801](https://github.com/vllm-project/vllm-ascend/pull/4161#issuecomment-3540994801),
and
[discussion_r2492593988](https://github.com/vllm-project/vllm-ascend/pull/3981#discussion_r2492593988),
all demonstrating thorough and insightful feedback.
- Sustained and Quality Contributions: His contributions reflect a
strong grasp of both vLLM and vLLM Ascend codebases, particularly in
prefill-decode disaggregation and KV pool areas ([7 PRs
merged](https://github.com/vllm-project/vllm-ascend/pulls?q=is%3Apr+author%3ALCAIZJ+is%3Amerged+)).
Prefill-Decode Disaggregation: Delivered KV transfer functionality using
Mooncake TransferEngine and enabled layerwise KV transfer
https://github.com/vllm-project/vllm-ascend/pull/1568
https://github.com/vllm-project/vllm-ascend/pull/2602
KV Pool: Developed the foundational KV Pool infrastructure and migrated
it to the latest ADXL stack
https://github.com/vllm-project/vllm-ascend/pull/2913
https://github.com/vllm-project/vllm-ascend/pull/3350
- Quality Contribution:
https://github.com/vllm-project/vllm-ascend/pull/1568
https://github.com/vllm-project/vllm-ascend/pull/2602
https://github.com/vllm-project/vllm-ascend/pull/2913
https://github.com/vllm-project/vllm-ascend/pull/3350
- Community Involvement:
He actively responds to [community
issues](https://github.com/vllm-project/vllm-ascend/issues?q=is%3Aissue%20commenter%3ALCAIZJ%20is%3Aopen%20-author%3ALCAIZJ),
continuously monitors functionality and accuracy issues related to PD
disaggregation and KV Pool, and proactively delivers [bug
fixes](https://github.com/vllm-project/vllm-ascend/pulls?q=is%3Apr+author%3ALCAIZJ+is%3Amerged+bugfix).
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
vLLM Ascend Plugin documents
Live doc: https://vllm-ascend.readthedocs.io
Build the docs
# Install dependencies.
pip install -r requirements-docs.txt
# Build the docs.
make clean
make html
# Build the docs with translation
make intl
# Open the docs with your browser
python -m http.server -d _build/html/
Launch your browser and open:
- English version: http://localhost:8000
- Chinese version: http://localhost:8000/zh_CN